Date of Award
Spring 5-9-2015
Degree Type
Thesis
Degree Name
Master of Arts (MA)
Department
Computer Science
First Advisor
Dr. Yanqing Zhang
Second Advisor
Dr. Zhipeng Cai
Third Advisor
Dr. Yingshu Li
Abstract
Traditional communication channels like news channels are not able to provide spontaneous information about disasters unlike social networks namely, Twitter. The present research work proposes a framework by mining real-time disaster data from Twitter to predict the path a disaster like a tornado will take. The users of Twitter act as the sensors which provide useful information about the disaster by posting first-hand experience, warnings or location of a disaster. The steps involved in the framework are – data collection, data preprocessing, geo-locating the tweets, data filtering and extrapolation of the disaster curve for prediction of susceptible locations. The framework is validated by analyzing the past events. This framework has the potential to be developed into a full-fledged system to predict and warn people about disasters. The warnings can be sent to news channels or broadcasted for pro-active action.
DOI
https://doi.org/10.57709/7009768
Recommended Citation
Jain, Saloni, "Real-Time Social Network Data Mining For Predicting The Path For A Disaster." Thesis, Georgia State University, 2015.
doi: https://doi.org/10.57709/7009768